Abstract: This paper proposes a mutation over-flow dealing method to fit for the environment of MOEAs，and applies these operators successfully to multi-objective optimization problems.Then it compares these operators’ performance through its convergence quality，and it demonstrates that this over-flow dealing method is effective through a group of experiments，the mutation operators used in single-objective optimization have the respectable distribution to polynomial mutation and achieve better convergence quality.

Key words: multi-objective optimization, Multi-Objective Evolutionary Algorithm（MOEA）, mutation operators, convergence quality, non-dominated solution sets

摘要： 提出了一种适应于多目标进化算法的变异越界处理策略，成功地将这些变异算子应用到多目标进化优化问题中，从多目标优化收敛性的角度比较了这些变异算子的性能。通过一组实验表明这种越界处理方法是非常有效的，单目标优化中的这些变异算子具有与多项式变异算子相当的分布性，同时取得了更好的收敛性能。

关键词: 多目标优化, 多目标进化算法, 变异算子, 收敛性, 非支配解集